The present invention relates to an automatic machine-learning high value generator, and more specifically, to an automatic machine-learning system that produces selections and combination of high value tests based on execution history and source change sets.
With the growth of software product development processes, testing of corresponding software products and related processes has also increased in sophistication such that the quantity of tests for each stage of the product development process has increased. The increase in test quantity causes the time to execute the tests and receive results to become unbearable for developers. In turn, these tests are not executed, which jeopardizes the quality of products, or the execution of these test detrimentally affects the agility and productivity of the software product development processes.